Anonymization of Sensitive Quasi-Identifiers for l-Diversity and t-Closeness

Sei, Yuichi; Okumura, Hiroshi; Takenouchi, Takao; Ohsuga, Akihiko

doi:10.1109/tdsc.2017.2698472

Cited by 65 publications

(41 citation statements)

References 36 publications

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“…Another study suggested that user's privacy cannot be effectively protected by assuming that QIs and SAs are different. In some cases, the QIs can behave as SA and can jeopardize the privacy of the user [52]. Social identity linkage [53], users profile cloning [54], group affiliation link disclosure [55], and community identities disclosures [56], among others, are possible threats on SN users released data.…”

Section: Background and Related Workmentioning

confidence: 99%

Attribute susceptibility and entropy based data anonymization to improve users community privacy and utility in publishing data

Majeed

Lee

2020

Appl Intell

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User attributes affect community (i.e., a group of people with some common properties/attributes) privacy in users' data publishing because some attributes may expose multiple users' identities and their associated sensitive information during published data analysis. User attributes such as gender, age, and race, may allow an adversary to form users' communities based on their values, and launch sensitive information inference attack subsequently. As a result, explicit disclosure of private information of a specific users' community can occur from the privacy preserved published data. Each item of user attributes impacts users' community privacy differently, and some types of attributes are highly susceptible. More susceptible types of attributes enable multiple users' unique identifications and sensitive information inferences more easily, and their presence in published data increases users' community privacy risks. Most of the existing privacy models ignore the impact of susceptible attributes on user's community privacy and they mainly focus on preserving the individual privacy in the released data. This paper presents a novel data anonymization algorithm that significantly improves users' community privacy without sacrificing the guarantees on anonymous data utility in publishing data. The proposed algorithm quantifies the susceptibility of each attribute present in user's dataset to effectively preserve users' community privacy. Data generalization is performed adaptively by considering both user attributes' susceptibility and entropy simultaneously. The proposed algorithm controls overgeneralization of the data to enhance anonymous data utility for the legitimate information consumers. Due to the widespread applications of social networks (SNs), we focused on the SN users' community privacy preserved and utility enhanced anonymous data publishing. The simulation results obtained from extensive experiments, and comparisons with the existing algorithms show the effectiveness of the proposed algorithm and verify the aforementioned claims.

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Section: Background and Related Workmentioning

confidence: 99%

Attribute susceptibility and entropy based data anonymization to improve users community privacy and utility in publishing data

Majeed

Lee

2020

Appl Intell

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“…Among them, anonymous trajectory sets are generated according to specific generalization patterns, including partition-based generalization [40], hierarchy-based generalization [41] and spatial generalization [42]. To achieve a better generalization effect, a quasiidentifier (QIDs) mechanism [43] using the timestamp as a quasi-identifier and a local extension mechanism [13] were proposed. Terrovitis et al [44] proposed a data compression technique based on the attacker's background knowledge to model a set of trajectory ions in the trajectory dataset.…”

Section: Related Work a Privacy-preserved Trajectory Data Publismentioning

confidence: 99%

Risk-Aware Individual Trajectory Data Publishing With Differential Privacy

et al. 2021

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“…Guo Hao and Xu Ya-Bin [20] improved t-closeness model using parameter selection and adjustment method of the anonymous method. Yuchi Sei et al [21] introduced two novel privacy models, namely, (l1, ………, lq)-diversity and (t1, ………, tq)closeness by considering all the attributes to have both sensitive and quasi characteristics. Zhen Tu et al [22] proposed a novel algorithm for protecting the trajectory of an individual against semantic and re-identification attack while reserving high data utility.…”

Section: Literature Reviewmentioning

confidence: 99%

Clustering Dissimilar Tuples: A Stronger Notion of Privacy

2020

IJETER

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Identity disclosure and attribute disclosure have always been a major concern while publishing data. k-anonymity tries to solve identity disclosure but doesn't prevent attribute disclosure which leads to homogeneity and background knowledge attack. Preserving privacy of an individual is becoming more challenging due to increasing number of homogeneity and background knowledge attacks. l-diversity model has been proposed to thwart these attacks but it doesn't fulfil its obligations. Several authors found l-diversity model to be inadequate, hence they put forth another model called t-closeness. Over the years, many investigations and experimentations conducted by various researchers shows that t-closeness does not provide a clear relationship between the threshold value t and information gain and it also shows that Earth mover's distance, a distance metric used by t-closeness model, becomes complex with multiple sensitive attributes. In view of this challenge, we propose a stronger notion of privacy called Clustering Dissimilar Tuples (CDT) to thwart homogeneity and background knowledge attack by formalizing the idea of processing the original dataset initially wherever these attacks possibly occur. These attacks are found to occur in the tuples of sensitive attributes. Hence CDT processes the tuples of sensitive attributes to form equivalence classes consisting of dissimilar tuples. Through experimental evaluations, we show that CDT is practical and can be implemented efficiently with minimum utility loss and maximum privacy gain.

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Anonymization of Sensitive Quasi-Identifiers for l-Diversity and t-Closeness

Cited by 65 publications

References 36 publications

Attribute susceptibility and entropy based data anonymization to improve users community privacy and utility in publishing data

Attribute susceptibility and entropy based data anonymization to improve users community privacy and utility in publishing data

Risk-Aware Individual Trajectory Data Publishing With Differential Privacy

Clustering Dissimilar Tuples: A Stronger Notion of Privacy

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